Summary of A Comparison Of Deep Learning Architectures For Spacecraft Anomaly Detection, by Daniel Lakey and Tim Schlippe
A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection
by Daniel Lakey, Tim Schlippe
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper compares the effectiveness of various deep learning architectures in detecting anomalies in spacecraft data. It investigates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based architectures for anomaly detection in space operations. The models were trained and validated using a comprehensive dataset sourced from multiple spacecraft missions. Results show that CNNs excel in identifying spatial patterns, LSTMs and RNNs are proficient in capturing temporal anomalies, and Transformer-based architectures showcase promising results, especially in subtle and long-duration anomalies. The paper also evaluates considerations such as computational efficiency, ease of deployment, and real-time processing capabilities. The choice of deep learning architecture depends on the nature of data, type of anomalies, and operational constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different types of artificial intelligence (AI) to see which one is best at finding problems with spacecraft data. They tested four types: CNNs, RNNs, LSTMs, and Transformers. Each model was trained on a big dataset from multiple space missions and then checked for how well it worked. The results show that different models are good at finding different kinds of problems. For example, some models are great at spotting patterns in the data, while others are better at catching problems that happen over time. The paper also looks at how easy each model is to use and how fast it can process information. |
Keywords
* Artificial intelligence * Anomaly detection * Deep learning * Lstm * Transformer
